The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It is devoted to the art and science of intelligent analysis of (usually big) data sets for meaningful (and previously unknown) insights. This conference will enable the sharing and learning of research and progress in the local context and new breakthroughs in data mining algorithms and their applications across all industries.

Since AusDM’02 the conference has showcased research in data mining, providing a forum for presenting and discussing the latest research and developments. Built on this tradition, AusDM’20 will facilitate the cross-disciplinary exchange of ideas, experience and potential research directions. Speciﬁcally, the conference seeks to showcase: Research Prototypes; Industry Case Studies; Practical Analytics Technology; and Research Student Projects. AusDM’20 will be a meeting place for pushing forward the frontiers of data mining in academia and industry. In this year, AusDM is pleased to be co-located with the 2020 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2020) in Canberra, Australia. The IEEE SSCI co-locates several symposia under one roof, each dedicated to a specific topic in the Computational Intelligence domain.

Submission Guidelines

We invite three types of submissions for AusDM’20:

• Research Track: Academic submissions reporting on new algorithms, novel approaches and research progress, with a paper length of between 8 and 12 pages in IEEE style, as detailed below.

• Application Track: Submissions reporting on applications of data mining and machine learning and describing speciﬁc data mining implementations and experiences in the real world. Submissions in this category can be between 6 and 12 pages in CCIS style.

• Industry Showcase Track: Submissions from governments and industry on an analytics solution that has raised profits, reduced costs and/or achieved other important policy and/or business outcomes can be made in this track with a paper length between 4 and 6 pages in CCIS style.

All submissions will go through a double-blind review process, i.e. paper submissions must NOT include authors names or affiliations or acknowledgments referring to funding bodies. Self-citing references should also be removed from the submitted papers for the double-blinded reviewing purpose. The information can be added in the accepted final camera-ready submissions.

All submissions are required to follow the format speciﬁed for papers in the CCIS style. Author guideline, LaTeX style file and Word template of the CCIS style can be found on AusDM’20 Submission Page (http://www.ieeessci2020.org/submission.html). The electronic submissions must be in PDF only and made through the SSCI’20 Submission Page by selecting the AusDM Academic track or AusDM Industry track as “Main Research Topic”.

Important Dates

Submissions: 7 August 2020 (no extension)

Notiﬁcation: 4 September 2020

Camera-ready: 18 September 2020

Conference: 1-4 December 2019

List of Topics

We are calling for papers, both research and applications, and from both academia and industry, for publication and presentation at the conference. All papers will go through double–blind, peer–review by a panel of international experts. The AusDM 2020 proceeding will be published by IEEE and become available immediately after the conference.

Please note that AusDM’20 requires that at least one author for each accepted paper register for the conference and present their work.

AusDM invites contributions addressing current research in data mining and knowledge discovery as well as experiences, novel applications and future challenges. Topics of interest include, but are not restricted to:

• Applications and Case Studies — Lessons and Experiences

• Big Data Analytics

• Biomedical and Health Data Mining

• Business Analytics

• Computational Aspects of Data Mining

• Data Integration, Matching and Linkage

• Data Mining in Education

• Data Mining in Security and Surveillance

• Data Preparation, Cleaning and Preprocessing

• Data Stream Mining

• Evaluation of Results and their Communication

• Implementations of Data Mining in Industry

• Integrating Domain Knowledge

• Link, Tree, Graph, Network and Process Mining

• Multimedia Data Mining

• New Data Mining Algorithms

• Professional Challenges in Data Mining

• Privacy-preserving Data Mining

• Spatial and Temporal Data Mining

• Text Mining• Visual Analytics

• Web and Social Network Mining

Committees

Conference Chairs:

• Rohan Baxter, Australian Taxation Office

• Richi Nayak, Queensland University of Technology

• Dharmendra Sharma, University of Canberra

Program Chairs:

• Mohammad.Abualsheikh, University of Canberra

• Yue Xu, Queensland University of Technology

• Yanchang Zhao, Data61, CSIRO

• Jin Li, Data2Action

Organising Chair

• Saber Mohamed Elsayed, University of New South Wales Canberra

Publicity Chair:

• Md Abul Bashar, Queensland University of Technology

Steering Committee:

• Simeon Simoﬀ (Chair), University of Western Sydney

• Graham William (Chair), Microsoft

• Peter Christen, The Australian National University

• Ling Chen, University of Technology

• Zahid Islam, Charles Sturt University

• Paul Kennedy, University of Technology

• Yun Sing Koh, The University of Auckland

• Jiuyong (John) Li, University of South Australia

• Richi Nayak, Queensland University of Technology

• Kok–Leong Ong, La Trobe University

• Dharmendra Sharma, University of Canberra

• Glenn Stone, Western Sydney University

• Yanchang Zhao, Data61, CSIRO

Invited Speakers

As is tradition for AusDM we have lined up an excellent keynote speaker program. Each speaker is a well-known research and/or practitioner in data mining and related disciplines. The keynote program provides an opportunity to hear from some of the world’s leaders on what the technology oﬀers and where it is heading.

Publication

The AusDM 2020 proceeding will be published by IEEE and become available immediately after the conference.